During evolution, proteins retain common three-dimensional structural features, even though the underlying sequence of amino acids can diverge dramatically. Such relationships can even be found internally within a given protein structure, arising from duplication and repetition of defined elements. Identifying relationships between two proteins, or two regions of the same protein, that are very distantly related, therefore, can be of extremely high value. Most often, identification of those relationships is needed on the level of primary amino acid codes, which is achieved by aligning their sequences. However, when structures have been determined, such relationships can be detected by overlaying common regions, a technique known as structure alignment. Both procedures involve considerable challenges, especially when the similarities between the two proteins are small. Consequently, there remains a need for methods that reliably and accurately compute sequence or structure alignments. In the past year, we have combined efforts from two different fronts in developing such tools. First, we have made improvements to our benchmark set of homologous membrane protein structures, earlier versions of which were called HOMEP. The code used to compile the dataset has been rewritten to make it more streamlined and able to run in parallel, allowing for fast future updates as the database of available membrane protein structures continues its pseudo-exponential growth. These changes will facilitate retraining of, and therefore improvements in, our sequence alignment software, AlignMe developed previously. Second, we have expanded upon an earlier (manual) analysis of symmetries in structures of membrane proteins, by initiating a systematic study that applies available symmetry analysis tools (SymD and CEsymm) to known protein structures. This work is expected to identify patterns and relationships in symmetrical and asymmetrical membrane proteins and to reveal how those symmetries relate to functional mechanisms. These two analyses have now been combined into a single database called EncoMPASS (Encyclopedia for Membrane Proteins Analyzed by Structure and Symmetry). The combination allows us to leverage information about structural neighbors in order to improve the quality and applicability of the symmetry analysis. Moreover, we have made visualization of the data easy through a public webserver hosted at https://encompass.ninds.nih.gov.